1.1 Motivation
Simulation of human face aging is a special task in facial editing. It aims at generating an elder face image from a young one. And it has many important applications, such as pursuing criminals, seeking missing children or face recognition system.
If a child has been missing or a criminal has fled for a long time. Their faces might change because of the aging process. The differences between the real person and the old pictures can increase the difficulty of face identification by the law enforcement agencies.
The same problem appears in the automatic face recognition system. Facial pictures in the database of recognition system were established earlier. But those pictures are difficult to be updated as time pass by. The accuracy of the face recognition system will decrease as the members in the database get older.
The goal of the human face aging simulation is to solve the above problems we listed. The technique of simulating the aging effect of face from the young pictures can help the law enforcement agencies to get the more reasonable pictures to pursue criminals or seek missing children. In other hand, the recognition rate of face recognition system will also increase.
The aging simulation can also be used in the aspect of entertainment. There are more and more video games that included the whole life of avatars (e.g. Fable or The Sims). If the video game can let players to set their own photos and the photos would get aging with time, it can
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make realistic effects.
Compared to the other image editing domains, there are some special characteristics in face aging simulation. For instance, every person's aging process is individual with others. One's aging process cannot be used to simulate other people directly, which means that there may not exist a general function for forecasting of human’s facial aging. Therefore, we propose the example-based algorithm to achieve a reasonable simulation by using an aging database and facial skin database.
1.2 Framework
As aforementioned, everyone has his/her own unique aging process. It is hard to use physical-based method to find a general aging variation for simulating all people. Therefore, we adopt a widely-use statistical method named “Principal Component Analysis” (PCA) [JOLL02] to find the appropriate aging effects in the aging database [FGN02].
The proposed method is inspired by Aging Pattern Subspace (AGES) algorithm [GZS07], presented by Geng et al. They use PCA and EM-like [DLD77] algorithm to extract the aging variation of aging database [FGN02] and full-fill the incomplete aging database for improving the accuracy rate of age estimation system originally.
In our system, we put the young face picture in the aging pattern. The aging database can be trained in the full-filling process in AGES algorithm. By doing this, the elder variation of face can be solved by the least squares solution with the training database. However, only one or two young pictures in the aging pattern is too less to generate the stable result. To solve this problem, we adjust the initial guess of the aging pattern by the available young data. In
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addition, we analyze the similarities between the target person and his or her parents, and enhance the PCA process by adding reasonable ratio of parents' aging process.
Although the PCA-based algorithm can predict the variation of facial geometries and skin textures, there are two main problems about the facial skin texture simulation. First, the skin textures are very blurry since the dimensional constrain of PCA-based method. The images in the database are composed of only about 5,000 pixels and it is grayscale image. Second, in the process of PCA method, the data of texture will be projected to the subspace which is composed by some important bases. And the facial detail (e.g. wrinkles and other creases) may disappear since the detail of skin is in high frequency domain.
However, the wrinkles and other creases are important visual elements in elder faces. We proposed a patch-based facial texture synthesis method to enhance the detail of facial skin.
This method is modified by Visio-lization algorithm [MPK09] proposed by Mohammed et al.
It is used for generating the novel facial images by synthesizing the facial patches in the database. We established a skin database with a lot of high resolution images at different ages.
And all facial images in database are divided to several patches. The low frequency elder skin generated by PCA will be compared with the skin images of database patch by patch. Then we keep all of the appropriate skin patches and combine them by solving the Poisson equalization [PGB03]. Finally, we extract the skin detail [LSZ01] to transfer to estimated skin image. After the two-stage method, we can generate an elder face image with the aging geometry and appropriate elder facial skin. To enhance the visual effect, we also compare the grayscale values between the target image and the images in the database and transfer the color of most appropriate image to target image finally.
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This paper is organized as follows. In the chapter 2, we introduce several aging simulation methods presently. the overview in our aging algorithm is described in chapter 3. About the facial aging prediction, we introduce the AGES algorithm and how parents' effects the prediction in the chapter 4. The facial texture synthesis and transferring is shown in the chapter 5. Also, we demonstrate the experiment results in the chapter 6. The chapter 7 is our conclusion and the future work. Figure 1.1 is the flowchart of our system.
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Figure 1. 1 The flowchart of our system
Input Image
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